class_docstr: Autoregressive forecasting model based on a recurrent neural network [Salinas2020]_.
  
  Based on `gluonts.torch.model.deepar.DeepAREstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.deepar.html>`_.
  See GluonTS documentation for additional hyperparameters.
  
  
  References
  ----------
  .. [Salinas2020] Salinas, David, et al.
      "DeepAR: Probabilistic forecasting with autoregressive recurrent networks."
      International Journal of Forecasting. 2020.
  
  
  Other Parameters
  ----------------
  context_length : int, default = max(10, 2 * prediction_length)
      Number of steps to unroll the RNN for before computing predictions
  disable_static_features : bool, default = False
      If True, static features won't be used by the model even if they are present in the dataset.
      If False, static features will be used by the model if they are present in the dataset.
  disable_known_covariates : bool, default = False
      If True, known covariates won't be used by the model even if they are present in the dataset.
      If False, known covariates will be used by the model if they are present in the dataset.
  num_layers : int, default = 2
      Number of RNN layers
  hidden_size : int, default = 40
      Number of RNN cells for each layer
  dropout_rate : float, default = 0.1
      Dropout regularization parameter
  embedding_dimension : int, optional
      Dimension of the embeddings for categorical features
      (if None, defaults to [min(50, (cat+1)//2) for cat in cardinality])
  distr_output : gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()
      Distribution to use to evaluate observations and sample predictions
  scaling: bool, default = True
      Whether to automatically scale the target values
  max_epochs : int, default = 100
      Number of epochs the model will be trained for
  batch_size : int, default = 64
      Size of batches used during training
  predict_batch_size : int, default = 500
      Size of batches used during prediction.
  num_batches_per_epoch : int, default = 50
      Number of batches processed every epoch
  lr : float, default = 1e-3,
      Learning rate used during training
  trainer_kwargs : dict, optional
      Optional keyword arguments passed to ``lightning.Trainer``.
  early_stopping_patience : int or None, default = 20
      Early stop training if the validation loss doesn't improve for this many epochs.
code: def _get_estimator_class(self) -> Type[GluonTSEstimator]:
      from gluonts.torch.model.deepar import DeepAREstimator
  
      return DeepAREstimator
  
imports: import logging
  from typing import Any, Dict, Type
  from gluonts.model.estimator import Estimator
  from autogluon.timeseries.models.gluonts.abstract_gluonts import AbstractGluonTSModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
label_desc: Use image node_id 1 for calling the DeepARModel obj's underlying member method code with example usage: obj._get_estimator_class() and returns: DeepAREstimator